Synthesizing average 3D anatomical shapes

Abstract

Average shape estimates are often used to characterize normal morphological variation and discriminate dysmorphology in populations. The purpose of this paper is to estimate "average" or the most representative shapes in populations by using high-resolution medical images as input. The "average" shape is computed from high-dimensional spatial transformations used to co-register each subject in the population rather than the image intensities. Inverse consistent image registration is used to help minimize correspondence errors and produce better population average estimates. Testing the method was done using a population of adult MR brain scans from 22 individuals with no know structural abnormalities. Population averages were computed using the spatial transformation method and local changes in morphology were mapped. Results suggest that this method is a feasible means for robust estimation of population average shape. It is also shown that using inverse consistent transformations produces average shape estimates with less error compared to those produced with transformations with nontrivial inverse consistent errors.